no code implementations • 25 Feb 2024 • Ruizhe Zhang, Qingyao Ai, Yiqun Liu, Yueyue Wu, Beining Wang
Gender of the defendants in both the task and relevant cases was edited to statistically measure the effect of gender bias in the legal case search results on participants' perceptions.
1 code implementation • 21 Feb 2024 • Prakamya Mishra, Zonghai Yao, Parth Vashisht, Feiyun ouyang, Beining Wang, Vidhi Dhaval Mody, Hong Yu
Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.
no code implementations • 16 Nov 2023 • Zonghai Yao, Ahmed Jaafar, Beining Wang, Zhichao Yang, Hong Yu
We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.
1 code implementation • 30 Oct 2023 • Prakamya Mishra, Zonghai Yao, Shuwei Chen, Beining Wang, Rohan Mittal, Hong Yu
In this work, we propose a new pipeline using ChatGPT instead of human experts to generate high-quality feedback data for improving factual consistency in the clinical note summarization task.
no code implementations • 7 Oct 2023 • Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.